REACH is a reinforcement‑learning based hierarchical framework that allows a Unitree G1 humanoid robot to navigate confined spaces by switching between variable‑height bipedal walking and crawling. The system learns policies that adjust body height from 1.27 m down to 0.20 m, uses a history‑conditioned latent representation to handle contact‑rich crawling dynamics, and incorporates a bidirectional transition policy to connect the two locomotion modes. A LiDAR‑based planner generates collision‑free 3D paths and proactive height targets, and real‑world tests confirm accurate height and velocity tracking, robust terrain traversal, and reliable mode transitions.
Humanoid locomotion in confined spaces requires continuous height adaptation across multiple locomotion modes, yet most existing approaches remain limited to height-adjustable bipedal walking. We pres...
arXiv:2606. 08253v1 Announce Type: cross Abstract: Enabling humanoid robots to operate in complex, dynamic environments remains a critical challenge, fundamentally limited by the ability to navigate robustly, safely, and accurately.
arXiv:2509. 26633v3 Announce Type: replace-cross Abstract: A dominant paradigm for teaching humanoid robots complex skills is to retarget human motions as kinematic references to train reinforcement learning (RL) policies.
The paper introduces ASTRIL-MPC, a language‑guided neural model predictive control framework that enables articulated tracked robots to navigate complex, contact‑rich urban environments such as stairwells and cluttered interiors. By combining a learned kinematics model that predicts short‑horizon state changes, an optimization‑based planner with multi‑objective costs, and a large language model that safely updates control weights, the system achieves up to 71% better traversal quality than non‑adaptive NMPC and 67% better than a PPO baseline, while eliminating collision impacts during descent. Real‑robot trials over four indoor obstacles confirm the method’s transferability to physical contact‑rich traversal.
arXiv:2601.07718v2 Announce Type: replace-cross Abstract: Achieving robust humanoid locomotion in complex, unstructured environments requires transitioning from reactive proprioception to proactive p...
arXiv:2609.13083v2 Announce Type: replace-cross Abstract: In urban search and rescue, articulated tracked robots (ATRs) must traverse structured but contact-rich environments such as stairwells and c...
arXiv:2609.09158v1 Announce Type: cross Abstract: We study the problem of navigating cluttered indoor environments with a humanoid robot. Unlike conventional methods that model navigation as a 2D pat...
arXiv:2603. 13707v3 Announce Type: replace-cross Abstract: Humanoid loco-manipulation requires coordinated task-space motion planning with stable loco-manipulation command tracking under complex robot-environment dynamics and long-horizon tasks.
FootQuery is a perceptive locomotion framework that retrieves depth information from a robot’s own history by querying each foot’s predicted next touchdown. The policy uses proprioceptive predictions of touchdown locations and uncertainties to sample relevant historical depth frames, fuses these per‑foot features with global visual memory, and generates control actions. In simulation and on a real Unitree G1 robot, FootQuery enables continuous traversal of complex outdoor stairs, indoor routes, platforms, and gaps, outperforming component ablations.
The paper introduces InterTrack, a behavior world model that enables humanoid robots to perform robust whole-body tracking while interacting with varied terrain and objects. Using a Transformer architecture, InterTrack predicts actions, states, and behavior distributions conditioned on the environment, and it is trained with an automated pipeline that reconstructs 3D support geometry from retargeted motions. The system achieves an 81.3% success rate on terrain interaction, a 99.3% fall-recovery rate, and outperforms leading baselines in both free-space tracking and cross-terrain scenarios.
The paper proposes a lightweight foothold prediction model (FPM) that anticipates future foot placements using only proprioception and velocity commands, allowing the robot to sample elevation maps only around predicted footholds. This preprocessing reduces redundant sensory input, enabling the locomotion policy to learn faster—reaching a given terrain difficulty in about one‑third the training iterations compared to using the full elevation map. The resulting policy outperforms both full‑map and attention‑based encoders on sparse foothold scenarios, and it zero‑shot transfers to a Unitree G1 humanoid across diverse indoor and outdoor obstacles.
arXiv:2609.22611v1 Announce Type: cross Abstract: Humanoid robots can acquire complex skills by imitating kinematic humanoid motion references, yet reliable references for contact-rich interactions r...